1. Field of the Invention
The present invention relates to a vehicle motion model generating device for generating a vehicle motion model which represents a motion state of a vehicle and a method for generating a vehicle motion model, and more specifically to a vehicle motion model using a recurrent neural network.
2. Description of the Related Art
Researches and developments of various techniques have been hitherto carried out in order to enhance the operational stability of vehicles. As one of these techniques is known a vehicle motion model generated by modeling the behavior of a vehicle based on the kinematic theory of a vehicle. The vehicle motion model is generated through experiments or simulations by modeling a yawing motion, a horizontal motion, a rolling motion, etc. of a vehicle under some running condition, and set based on equations for motion of the vehicle. The operational stability of the vehicle can be estimated by analyzing the behavior of the vehicle, that is, the motion state of the vehicle or a vehicle motion state based on the vehicle motion model.
Furthermore, a road surface friction coefficient is used for the operation of control parameters in order to perform vehicle control such as traction control, braking force control, torque distributing control or the like. Thus, there has been proposed a technique of using the vehicle motion model as described above and estimating the road surface friction coefficient based on the vehicle motion model and the actual vehicle motion state. For example, JP-A-8-2274 discloses a method of estimating the road surface friction coefficient by using adaptive control. In addition, JP-A-10-242030 discloses a method of estimating the road surface friction coefficient by comparing a vehicle slipping angle estimated by an observer with a reference value on a high μ-road based on the vehicle motion model and a reference value on a low μ-road based on the vehicle motion model.
JP-A-4-138970 discloses a method of estimating the motion state of the vehicle by using a neural network. Specifically, a side slipping angle and a yaw rate are estimated on the basis of a neural network in which an easily-measurable parameters (for example, a longitudinal acceleration, a lateral acceleration, a vertical acceleration, a stirring torque, a front-wheel rudder angle, a vehicle speed, a rear-wheel rudder angle, etc.) in the vehicle parameters representing the motion state of the vehicle are set as inputs and difficultly-measurable parameters (a side slipping angle and a yaw rate) are set as outputs. Furthermore, JP-A-6-286630 discloses a method of estimating the road surface friction coefficient based on the running condition of the vehicle detected by using the neural network. According to the JP-A-4-138970 and JP-A-6-286630, in the neural network, adjustment (learning) of the coupling weight coefficient is carried out in advance according to an algorithm such as back propagation so that the output corresponds to a teaching signal.
According to the method described in the JP-A-8-2274 or JP-A-10-242030, when a vehicle motion model is set, a motion equation is linearly approximated to avoid cumbersome operation processing in the solution calculating process. Therefore, the vehicle motion model may not accurately represent the motion state of the vehicle, that is, the behavior of the vehicle in a non-linear region.
Furthermore, according to the method described in the JP-A-4-138970, a feed-forward type neural network is used, the value output from the neural network and the value input to the neural network are independent of each other. Thus, the motion state of the vehicle may not be accurately represented in such a neural network. In particular, the values output from the neural network (for example, the side slipping angle and the yaw rate) are varied in accordance with not only the input, but also the value thereof at the present time (a present value). Consequently, it is necessary to feed back the output value and reflect the output value to the neural network, in order to estimate the motion state of the vehicle with high precision. In this sense, the JP-A-6-286630 supplies the time delay value of the output value to an input layer by using an ARAM model, thereby enhancing the estimation precision of the road surface friction coefficient. However,the neural network having such feedback has a problem that the coupling weight coefficient cannot be learned according to the principle of a learning rule such as back propagation. Thus, accurate estimation of the road surface friction coefficient is hardly achieved.
The present invention has been implemented in view of the foregoing situation, and has an object to provide a novel method of creating a motion model of a vehicle by using a recurrent neural network containing a feedback loop.